针对物联网异常检测中数据分布变化，提出轻量级持续学习框架
Online Continual Learning for Anomaly Detection in IoT under Data Distribution Shifts
- 设备端智能选样，边缘端检测分布偏移，协同更新模型
- 实验显示精度高，模型更新次数比基线减少超50%
- 适合资源受限的物联网场景，尤其适用于动态环境
本文提出OCLADS，一种面向物联网异常检测的持续学习通信框架，应对非平稳环境下的数据分布漂移问题。随着观测数据统计特性随时间变化，本地推理模型会失效，需策略性地进行更新。OCLADS通过追踪数据分布变化，实现对物联网设备端异常检测模型的及时更新。该框架在资源受限的物联网设备与边缘服务器（ES）间交互时引入两项机制：一是在设备端实施智能样本选择以优化数据传输；二是在边缘服务器端部署分布偏移检测机制以触发模型更新。基于TinyML的实验表明，所提框架在保持高推理精度的同时，显著减少了模型更新次数，优于基准方案。
原文摘要 · Abstract (English)
In this work, we present OCLADS, a novel communication framework with continual learning (CL) for Internet of Things (IoT) anomaly detection (AD) when operating in non-stationary environments. As the statistical properties of the observed data change with time, the on-device inference model becomes obsolete, which necessitates strategic model updating. OCLADS keeps track of data distribution shifts to timely update the on-device IoT AD model. To do so, OCLADS introduces two mechanisms during the interaction between the resource-constrained IoT device and an edge server (ES): i) an intelligent sample selection mechanism at the device for data transmission, and ii) a distribution-shift detection mechanism at the ES for model updating. Experimental results with TinyML demonstrate that our proposed framework achieves high inference accuracy while realizing a significantly smaller number of model updates compared to the baseline schemes.
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